4.2.1.1. nilearn.image.high_variance_confounds

nilearn.image.high_variance_confounds(niimgs, n_confounds=5, percentile=2.0, detrend=True, mask_img=None)

Return confounds signals extracted from input signals with highest variance.

Parameters :

niimgs: niimg :

4D image.

mask_img: niimg :

If provided, confounds are extracted from voxels inside the mask. If not provided, all voxels are used.

n_confounds: int :

Number of confounds to return

percentile: float :

Highest-variance signals percentile to keep before computing the singular value decomposition, 0. <= percentile <= 100. mask_img.sum() * percentile / 100. must be greater than n_confounds.

detrend: bool :

If True, detrend signals before processing.

Returns :

v: numpy.ndarray :

highest variance confounds. Shape: (number of scans, n_confounds)

See also

nilearn.signal.high_variance_confounds

Notes

This method is related to what has been published in the literature as ‘CompCor’ (Behzadi NeuroImage 2007).

The implemented algorithm does the following:

• compute sum of squares for each signals (no mean removal)
• keep a given percentile of signals with highest variance (percentile)
• compute an svd of the extracted signals
• return a given number (n_confounds) of signals from the svd with highest singular values.